Hacker News Year-in-Review Summarizer
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Generates a historical overview of Hacker News headlines for a specific day across multiple years, highlighting tech trends and categorizing key stories.
About This Workflow
This workflow automates the process of generating a comparative summary of Hacker News front-page headlines for a particular day across various years. It leverages AI to analyze historical tech trends and present them in a structured, human-readable format, complete with categorized headlines and hyperlinks.
Key Features
- Historical Trend Analysis: Identifies how the tech landscape has evolved by comparing headlines from the same day across multiple years.
- Headline Categorization: Organizes top headlines into thematic categories.
- AI-Powered Summarization: Utilizes a language model to generate a concise overview and thematic summaries.
- Markdown Formatting: Outputs results in Markdown with hyperlinked headlines, including the year prefix.
- Automated Data Fetching: Retrieves Hacker News headlines programmatically.
- Customizable Date Range: Fetches data from 2007 up to the current year for a given day.
- Telegram Integration: Automatically posts the generated summary to a Telegram channel.
How To Use
- Schedule Trigger: Configure the
Schedule Triggernode to run the workflow at your desired time (e.g., daily at 9 PM). - CreateYearsList: This node automatically determines the range of years to fetch data for, starting from the current year and going back to 2007, ensuring that headlines from the same day across different years are collected.
- CleanUpYearList & SplitOutYearList: These nodes prepare the list of dates for subsequent processing.
- GetFrontPage: This
HTTP Requestnode fetches the Hacker News front page for each generated date. - ExtractDetails: The
HTMLnode parses the fetched HTML to extract headlines and their corresponding URLs, as well as the date. - GetHeadlines & GetDate: These
Setnodes are used to structure the extracted headline and date information. - MergeHeadlinesDate: This node combines the extracted headlines and dates into a unified dataset.
- SingleJson: The
Aggregatenode consolidates all the data into a single JSON object. - Basic LLM Chain: This
Langchain Chainnode takes the consolidated JSON data and, using theGoogle Gemini Chat Model, generates a comprehensive summary in Markdown format, categorizing headlines and analyzing tech trends. - Telegram: The
Telegramnode sends the final Markdown summary to the specified Telegram channel (@OnThisDayHN).
Apps Used
Workflow JSON
{
"id": "27523fbe-9d9e-42fb-aab1-12709ffeacec",
"name": "Hacker News Year-in-Review Summarizer",
"nodes": 13,
"category": "Data Analysis",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 27523fbe-9d9e...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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